Engineering complex embedded systems with state analysis and the mission data system
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This paper is one of a set of papers, developed simultaneously and presented within a single conference session, that are intended to highlight systems analysis and design capabilities within the Systems Analysis and Concepts Directorate (SACD) of the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC). This paper focuses on the specific capabilities of uncertainty/risk analysis, quantification, propagation, decomposition, and management, robust/reliability design methods, and extensions of these capabilities into decision analysis methods within SACD. These disciplines are discussed together herein under the name of Decision Support Methods and Tools. Several examples are discussed which highlight the application of these methods within current or recent aerospace research at the NASA LaRC. Where applicable, commercially available, or government developed software tools are also discussed
The Global Modeling and Assimilation Office (GMAO) at NASA, GSFC is developing an integrated Earth system analysis (IESA). Integral to this IESA are ocean-atmosphere interface states, such as the skin SST. Recently the GMAO's near-real time operational weather analysis and prediction system implemented an analysis for skin SST along with the meteorological analysis since Jan, 2017. The skin SST is modeled and constrained using infrared radiometric observations. This poster describes some of the details of this development, its impact on forecasts, current and future developments towards the inclusion of microwave data (e.g. GPM-GMI).
The System Analysis Module (SAM) is a modern system analysis tool being developed at Argonne National Laboratory for advanced non-LWR safety analysis. It aims to provide fast-running, whole-plant transient analyses capability with improved-fidelity for SFR, LFR, and MSR/FHR. In prior work, SAM has performed well in modeling the EBR-II benchmark based on the SHRT-17 and SHRT-45R protected and unprotected loss-of-flow experiments. However, underlying uncertainties in the modeling parameters need to be quantified to gain confidence in any best-estimate simulations. In this work, a Python coupling framework between Dakota and SAM has been developed to investigate these uncertainties, alongside code enhancements in SAM for user-flexibility in perturbing physical closure models and fluid equations of state. 25 significant input parameters covering boundary conditions, geometry, physical closure models, and equations of state were identified with their uncertainties defined from literature. Two SAM transient models for SHRT-17 and SHRT-45R were perturbed on these input parameters using Latin Hypercube Sampling and the uncertainties in predicted maximum fuel, cladding, and coolant temperatures were quantified as the output responses. Spearman rank correlation of input parameters to the predicted temperatures indicated that the most sensitive parameters include the initial core power, pipe wall drag, fuel & coolant conductivity, and peak channel flow area. Sensitivity of input parameters was found to differ between the two transients and core channel modeling strategies. The overall perturbations did not result in large uncertainties in the predicted temperatures, despite the efforts made to obtain accurate yet conservative input uncertainties.
This paper proposes a time-domain electromechanical co-simulation framework to accomplish concurrent running of multiple simulators in order to preserve user-defined models and allow maximum simulation accuracy for power system transient analysis. Here, a middleware interface is developed to manage the interaction and synchronization of the multiple simulators via boundary model. Consequently, it eliminates the requirement of model conversion of each subsystem and thus ensure the user-defined models in each specific software are retained for entire system situational awareness. Preliminary testing results on 140-bus Northeastern Power Coordinating Council (NPCC) model validate the effectiveness and accuracy of the proposed framework for PSS/E and PSLF co-simulation.
To be an effective tool for resource analysis and planning, a geographic information system (GIS) needs to be integrated with a digital remote sensing capability. To be truly functional, the paired system must be driven by grass roots local needs. A case study couched in a Soil Conservation District in northern Utah is presented. Agency representatives determined that the most fundamental data sets to be entered into the GIS system analysis system in the first round were: land use/land cover; geomorphic/soil unit data; hydrologic unit data; and digital terrain. The least expensive and best ways to obtain these data were determined. Data were acquired and formatted to enter the state's PRIME/ARC-INFO GIS, and are being interrogated for resource management decisions related to such issues as agricultural preservation, urban expansion, soil erosion control, and dam siting.
This paper examines some important relationships, related with the system efficiency, for very high power, radio frequency solid-state transmitter; incorporating multiple solid-state power amplifier modules, power combiners, dividers, couplers, and control/interlock hardware. In particular, the characterization of such transmitters, at the component as well as the system level, is discussed. The analysis for studying the influence of the amplitude and phase imbalance, on useful performance parameters like system efficiency and power distribution is performed. This analysis is based on a scattering parameter model. This model serves as a template for fine-tuning the results, with the help of a system level simulator. For experimental study, this approach is applied to a recently designed modular and scalable solid-state transmitter, operating at the centre frequency of 505.8 MHz and capable of delivering a continuous power of 75 kW. Such first time presented, system level study and experimental characterization for the real time operation will be useful for the high power solid-state amplifier designs, deployed in particle accelerators.
Among the maintenance resources, the spare parts are the most difficult to predict. Items in the space systems are very different from the point of view of reliability, cost, weight, volume, etc. The different combinations of spares make different contribution to the: mission success, spare investment, volume occupied and weight. Hence, the selection of spares for a mission planned must take into account all of these features. This paper presents the generic mission success driven sparing model developed, for the complex space systems. The mathematical analysis used in the model enables the user to select the most suitable selection of the spare package for the mission planned. The illustrative examples presented clearly demonstrate the applicability and usefulness of the model introduced.
Efforts concentrated on development and implementation of the SolarSoft (SSW) data analysis system. From an EIT analysis perspective, this system was designed to facilitate efficient reuse and conversion of software developed for Yohkoh/SXT and to take advantage of a large existing body of software developed by the SDAC, Yohkoh, and SOHO instrument teams. Another strong motivation for this system was to provide an EIT analysis environment which permits coordinated analysis of EIT data in conjunction with data from important supporting instruments, including Yohkoh/SXT and the other SOHO coronal instruments; CDS, SUMER, and LASCO. In addition, the SSW system will support coordinated EIT/TRACE analysis (by design) when TRACE data is available; TRACE launch is currently planned for March 1998. Working with Jeff Newmark, the Chianti software package (K.P. Dere et al) and UV /EUV data base was fully integrated into the SSW system to facilitate EIT temperature and emission analysis.
The latest iterations of advanced reactor designs have included increased reliance on passive safety systems to maintain plant integrity during unplanned sequences. While these systems are advantageous in reducing the reliance on human intervention and availability of power, the phenomenological foundations on which these systems are built require a novel approach to a reliability assessment. Passive systems possess the unique ability to fail functionally without failing physically, a result of their explicit dependency on existing boundary conditions that drive their operating mode and capacity. Argonne National Laboratory is performing ongoing analyses that demonstrate various methodologies for the characterization of passive system reliability within a probabilistic framework. Two reliability analysis techniques are utilized in this work. The first approach, the Reliability Method for Passive Systems, provides a mechanistic technique employing deterministic models and conventional static event trees. The second approach, a simulation-based technique, utilizes discrete dynamic event trees to treat time- dependent phenomena during scenario evolution. For this demonstration analysis, both reliability assessment techniques are used to analyze an extended station blackout in a pool-type sodium fast reactor (SFR) coupled with a reactor cavity cooling system (RCCS). This work demonstrates the entire process of a passive system reliability analysis, including identification of important parameters and failure metrics, treatment of uncertainties and analysis of results.
The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.
The potential expansion of biofuel production raises food, energy, and environmental challenges that require careful assessment of the impact of biofuel production on greenhouse gas (GHG) emissions, soil erosion, nutrient loading, and water quality. In this study, we describe a spatially explicit integrative modeling framework (SEIMF) to understand and quantify the environmental impacts of different biomass cropping systems. This SEIMF consists of three major components: (1) a geographic information system (GIS)-based data analysis system to define spatial modeling units with resolution of 56 m to address spatial variability, (2) the biophysical and biogeochemical model Environmental Policy Integrated Climate (EPIC) applied in a spatially-explicit way to predict biomass yield, GHG emissions, and other environmental impacts of different biofuel crops production systems, and (3) an evolutionary multiobjective optimization algorithm for exploring the trade-offs between biofuel energy production and unintended ecosystem-service responses. Simple examples illustrate the major functions of the SEIMF when applied to a nine-county Regional Intensive Modeling Area (RIMA) in SW Michigan to (1) simulate biofuel crop production, (2) compare impacts of management practices and local ecosystem settings, and (3) optimize the spatial configuration of different biofuel production systems by balancing energy production and other ecosystem-service variables. Potential applications of the SEIMF to support life cycle analysis and provide information on biodiversity evaluation and marginal-land identification are also discussed. The SEIMF developed in this study is expected to provide a useful tool for scientists and decision makers to understand sustainability issues associated with the production of biofuels at local, regional, and national scales.
This presentation describes nuclear-renewable hybrid energy systems (N-R HESs), states their potential benefits, provides figures for the four tightly coupled N-R HESs that NREL is currently analyzing, and outlines the analysis process that is underway.
The purpose of this project is joint verification and validation of system analysis code capabilities for Sodium-cooled Fast Reactor (SFR) design and licensing. As the researchers and code developers at the Argonne National Laboratory (ANL) and China Institute of Atomic Energy (CIAE) independently build their own SFR system analysis capabilities, this collaboration facilitated exchange of information on, and evaluation of alternative approaches for, modeling the key phenomena (such as decay heat removal and natural circulation) and the safety-related components (such as pumps, heat exchangers, and outlet plenum) common in SFR systems. The analysis at ANL and CIAE also applied their respective computational capabilities and methods (in SAS4A/SASSYS-1 computer code at ANL and FR-Sdaso computer code at CIAE) on jointly identified benchmark problems for verification, and publicly available integral tests for validation, of their results. ANL team also shared the expertise on the established software quality assurance practices and the techniques for component reliability estimates to support probabilistic risk assessments.
Dual column ambient temperature system, consisting of pair of capillary columns, microbead thermistor detector and micro gas-sampling valve, is used in remote life-detection equipment for space experiments. Performance outweighs advantage gained by utilizing single-column systems to reduce weight, conserve carrier gas and operate at lower power levels.
The primary objective was to examine from a system safety viewpoint in-space propellant logistic elements and operations to define the potential hazards and to recommend means to reduce, eliminate or control them. A secondary objective was to conduct trade studies of specific systems or operations to determine the safest of alternate approaches.
The purpose of the Flight Experiment Demonstration System (FEDS) was to show, in a simulated spacecraft environment, the feasibility of using a microprocessor to automate the onboard orbit determination functions. The software and hardware configuration used to support FEDS during the demonstration and the results of the demonstration are discussed.
The Geophysical Processor System (GPS) at the Alaska (U.S.) SAR (Synthetic Aperture Radar) Facility (ASF) uses ERS-1 SAR images as input to generate three types of products: sea ice motion, sea ice type, and ocean wave spectra. The GPS, operating automatically with minimal human intervention, delivers its output to the Archive and Catalog System (ACS) where scientists can search and order the products on line. The GPS has generated more than 10,000 products since it became operational in Feb. 1992, and continues to deliver 500 new products per month to the ACS. These products cover the Beaufort and Chukchi Seas and the western portion of the central Arctic Ocean. More geophysical processing systems are needed to handle the large volumes of data from current and future satellites. Images must be routinely and consistently analyzed to yield useful information for scientists. The current GPS is a good, working prototype on the way to more sophisticated systems.